Contour Grouping with Partial Shape Similarity

  • Authors:

  • Chengqian Wu;Xiang Bai;Quannan Li;Xingwei Yang;Wenyu Liu

  • Affiliations:

  • Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, P.R. China 430074;Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, P.R. China 430074;Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, P.R. China 430074;Dept. of Computer and Information Sciences, Temple University, Philadelphia,;Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, P.R. China 430074

  • Venue:
  • PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
  • Year:
  • 2009

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Abstract

In this paper, a novel algorithm is introduced to group contours from clutter images by integrating high-level information (prior of part segments) and low-level information (paths of segmentations of clutter images). The partial shape similarity between these two levels of information is embedded into the particle filter framework, an effective recursively estimating model. The particles in the framework are modeled as the paths on the edges of segmentation results (Normalized Cuts in this paper). At prediction step, the paths extend along the edges of Normalized Cuts; while, at the update step, the weights of particles update according to their partial shape similarity with priors of the trained contour segments. Successful results are achieved against the noise of the testing image, the inaccuracy of the segmentation result as well as the inexactness of the similarity between the contour segment and edges segmentation. The experimental results also demonstrate robust contour grouping performance in the presence of occlusion and large texture variation within the segmented objects.